Dynamic monitoring, evaluation and prediction method of corn drought and flood based on fine grid water balance

Abstract

The present invention belongs to the field of agricultural technology, and establishes a dynamic monitoring and evaluation method and prediction method for corn drought and flood based on fine grid water balance. Acquire the actual agricultural meteorological data, forecast data, geographic data and agricultural data of the region; build a database; pre-process the training set; construct a water balance parameter model; construct a daily dynamic water profit and loss rate model; obtain the continuous rolling daily rainfall contribution weight and construct a daily dynamic farmland actual water supply index model; construct a daily dynamic drought and flood index model; obtain a daily corn field drought and flood level threshold system; monitor the daily dynamic drought and flood disaster drought and flood index; forecast the daily dynamic drought and flood disaster drought and flood index; correspond the forecast results of the drought and flood index with the daily corn field drought and flood level threshold, and then guide disaster prevention decisions. Solve the current sparse monitoring network problem to improve the spatiotemporal resolution and accuracy of disaster monitoring and evaluation.

Description

Technical Field

[0001] The invention belongs to the technical field of agriculture, and in particular relates to a method for dynamically monitoring, evaluating and predicting corn drought and flood based on fine grid water balance. Background Art

[0002] The growth and development of crops depends on meteorological conditions. When meteorological conditions cannot meet the needs of crop growth, it will lead to reduced crop yields, lower quality, and reduced farmers' income. The types of meteorological disasters in Northeast China are complex and diverse. The most serious and frequent impacts on agricultural production are corn drought and waterlogging. The annual frequency of drought and waterlogging disasters in most counties and cities in the main corn producing areas is generally around 50%, and the reduction in production is more than 10%. Therefore, timely monitoring, degree assessment, and drought and waterlogging prediction of corn fields are very important for preventing and reducing the negative impact of disasters on corn growth and maturity. From the perspective of disaster risk assessment, it is also necessary to conduct a comprehensive assessment of the risk of farmland drought disasters.

[0003] The agricultural meteorological monitoring and forecasting business of drought and flood has a history of several decades, but there are currently major problems: first, the network of meteorological and agricultural monitoring stations is sparse, and the information based on county-level meteorological stations is far from meeting the needs of modern agricultural production disaster prevention and mitigation; second, the monitoring and technical service methods are qualitative and static, and not quantitative and dynamic enough; third, they are mainly based on manual calculation and analysis, and have not achieved intelligence or digitization; fourth, the traditional disaster prevention and mitigation information dissemination method is backward and not suitable for modern information dissemination, and is far from meeting the needs of agricultural refined service level and disaster prevention and mitigation. Summary of the invention

[0004] The present invention provides a method for dynamic monitoring and evaluation of corn drought and flood based on fine grid water balance, which is used to solve the current sparse problem of corn drought and flood monitoring network and to improve the technical problem of temporal and spatial resolution and accuracy of disaster monitoring and evaluation.

[0005] The present invention provides a method for dynamically predicting corn drought and flood based on fine grid water balance, which is used to improve the drought and flood disaster prediction and defense information service capabilities.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for dynamic monitoring and evaluation of corn drought and flood based on fine grid water balance, the evaluation method comprising the following steps:

[0008] Step 1: Obtain the actual data, forecast data, geographic data and agricultural data of the region;

[0009] Step 2: Classify and store the data obtained in step 1 and build a database;

[0010] Step 3: Divide the data in the database in step 2 into a test set and a training set, and preprocess the training set;

[0011] Step 4: Construct a water budget parameter model based on different terrains of Gangpingwa;

[0012] Step 5: Based on the Penman algorithm, a polynomial fitting analysis method is used to construct a daily dynamic water requirement model for corn in the central area of ​​a certain province in Northeast China for the training set preprocessed in step 3;

[0013] Step 6: Based on steps 3 and 4, the interpolation analysis method is used to obtain the rainfall of farmland at the fine grid level;

[0014] Step 7: Construct a daily dynamic water surplus and deficit rate model based on steps 4 and 5;

[0015] Step 8: Based on step 7, the training set preprocessed in step 3 is subjected to a linear method to obtain the continuous rolling daily rainfall contribution weight and construct a daily dynamic farmland actual water supply index model;

[0016] Step 9: Construct a daily dynamic drought and flood index model based on steps 5 and 8;

[0017] Step 10: Based on the existing monthly corn drought and flood level threshold system and the industry standards of the China Meteorological Administration and the Ministry of Agriculture, the test set data of step 3 is substituted into the daily dynamic drought and flood index model of step 9 for calculation to obtain the daily corn field drought and flood level threshold system;

[0018] Step 11: Based on the actual data in the test set data of step 3, substitute the daily dynamic drought and flood index model of step 9 for calculation to obtain the monitoring of the daily dynamic drought and flood disaster drought and flood index.

[0019] Furthermore, the step 1 is specifically that the meteorological elements of the actual data and forecast data include: temperature, maximum temperature, minimum temperature, precipitation, wind speed, maximum wind speed and maximum wind speed;

[0020] The geographic data includes a data spatial scale;

[0021] The agricultural data mentioned above refers to 30 years of corn production, disasters and growth and development data obtained by referring to the annual crop data of the county annals.

[0022] Furthermore, the step 2 specifically includes classifying and storing the data acquired in step 1 according to actual data, forecast data, geographic data and agricultural data, and performing multi-data fusion on the stored data;

[0023] Specifically, step 3 is to divide the fused data into a test set and a training set, and perform data preprocessing on the training set using an interpolation analysis method.

[0024] Furthermore, the step 4 is specifically to obtain the village-level elevation data of the analysis area and establish a water budget parameter model for large, medium and small terrains at each level:

[0025]

[0026] Where: α is the water budget parameter of the plot, α<1, high altitude, hilly land, less effective water; α>1, low altitude, low terrain, more effective water; h js is the mean value of the elevation data of the county where the plot is located; h jx is the mean value of the elevation data of the township level area where the plot is located; h jc h is the mean value of the elevation data of the village-level plot where the plot is located; j is the elevation data of the plot; e is the empirical coefficient; m is the number of plots in the village.

[0027] Furthermore, in the case of no measuring station, the step 5 is to use the Penman algorithm to calculate and construct a daily dynamic water demand model based on the daily calculation results of the county-level meteorological station:

[0028]

[0029] Where: W k The daily sliding water demand for the stage;

[0030] W i =4E -12 i 6 -2E -9 i 5 +3E -7 i 4 -E -5 i 3 +E -5 i 2 +0.0455i+0.1276 (3)

[0031] Where: i is the day sequence value, which takes values ​​(1, 2, ..., d), where d = 365 during a common year and d = 366 during a leap year;

[0032] in:

[0033] W=E0·λ (4)

[0034] Where: λ is the corn crop coefficient, that is, the ratio of corn water demand to evapotranspiration during the same period; E0 is the reference evapotranspiration, which is calculated daily according to the Penman method:

[0035]

[0036] Where: R nis the net radiation on the crop surface; G is the soil heat flux; T is the daily average temperature at a height of 2 meters; u2 is the wind speed at a height of 2 meters; e s is the saturated water vapor pressure; e a is the actual water vapor pressure; e s -e a is the saturated water vapor pressure difference; Δ is the slope of the saturated water vapor pressure curve; r is the hygrometer constant.

[0037] Furthermore, the step 7 is specifically as follows:

[0038] Step 7.1: Conduct targeted analysis on different agro-climatic zones through agro-geographic cluster analysis.

[0039] The actual water supply of farmland is obtained based on the water budget parameter model. After the soil moisture of Gangpingwa is processed, the actual water supply of farmland is obtained, which is calculated as follows:

[0040] U i =α×V i +G i (i=1,2,...) (6)

[0041] Where: U is the actual water supply of farmland; V is the rainfall of farmland; G is the irrigation amount; i is the daily sequence value;

[0042] Step 7.2: Calculate the daily sliding water surplus and deficit based on the actual water supply and water demand of the farmland:

[0043]

[0044] H k =R k -W k (8)

[0045] Where: H is the daily sliding moisture surplus or deficit in the stage, H≥0 moisture surplus; H<0 moisture deficit; R k is the actual water supply of farmland sliding every day in each stage; k is the stage number; W k The daily sliding water demand for the stage;

[0046] Furthermore, the moisture profit and loss rate model is calculated based on the daily sliding moisture profit and loss amount:

[0047]

[0048] Where: P k It is the water surplus and deficit ratio in the daily sliding stage;

[0049] Furthermore, the step 8 is specifically to obtain the daily rainfall impact weight of farmland water supply based on step 7 and the linear relationship between the increase and decrease of farmland water and time, so as to construct a 21-day sliding farmland actual water supply index model:

[0050]

[0051] Where: S k is the actual water supply index of farmland in the current period, q is the number of consecutive rolling days, σ q is the weight coefficient corresponding to the number of consecutive rolling days, U kq It is the actual water supply to farmland corresponding to the consecutive rolling days.

[0052] Further, step 9 is specifically to obtain a drought and flood index based on step 5 and step 8. The drought and flood index is obtained by subtracting the daily sliding water demand of the stage from the actual water supply index of the farmland in the stage, and then dividing it by the daily sliding water demand of the stage. That is, the daily sliding drought and flood index model is specifically:

[0053]

[0054] Where: γ is the stage drought and flood index.

[0055] A method for dynamic prediction of corn drought and flood based on fine grid water balance, the method using the above-mentioned method for dynamic monitoring and prediction of corn drought and flood based on fine grid water balance of different terrains, further comprising step 12: substituting the actual data and forecast data in the test set data of step 3 into the daily dynamic drought and flood index model of step 9 for calculation, so as to realize the forecast of daily dynamic drought and flood disaster drought and flood index;

[0056] Step 13: Based on the prediction result of the drought and flood index in step 12 and the daily corn field drought and flood level threshold in step 10, the disaster level is determined to provide guidance.

[0057] Furthermore, the step 13 is specifically to calculate the predicted value of the daily drought and flood index for the next 7 days based on the daily numerical weather forecast results, and obtain guidance for daily dynamic drought and flood warning disasters.

[0058] The beneficial effects of the present invention are:

[0059] The present invention significantly improves the quantitative monitoring, assessment and forecasting service capabilities of corn drought and flood disasters, and greatly enhances the modernization level of agricultural information services and economic and social benefits.

[0060] The present invention improves the service channels and dissemination means of disaster prevention and reduction information, and makes the disaster prevention information service digital, intelligent and refined.

[0061] The present invention can improve the level of agricultural drought and flood disaster defense and reduce disaster losses. After promotion and application, it will make significant contributions to increasing agricultural production and income, realizing the "100 billion jin" grain project, taking the lead in realizing agricultural modernization and rural revitalization in our province, and can generate very significant economic and social benefits every year.

[0062] Based on the actual situation of a certain province in Northeast China, the present invention has refined the new disaster monitoring, assessment and prediction work from the previous county and city level to the village level, with a spatial resolution of 1 kilometer, and realized the meteorological, soil and corn growth monitoring at different growth stages in different terrains (hills, flats and depressions), dynamic disaster warning, assessment and yield forecasting.

[0063] The present invention is based on the crop growth characteristics and the influence of climatic conditions, and the monitoring, evaluation and forecasting and early warning of crop disasters such as drought and flood are developed from the original qualitative evaluation and trend qualitative forecasting to quantitative evaluation and quantitative forecasting.

[0064] The present invention realizes intelligent collection, processing and analysis of multi-source data through a multi-layer system environment including intelligent monitoring grid, database, big data technology, data interface service, cloud computing, application service, user service, etc.

[0065] The present invention establishes a new drought and flood assessment method based on water balance to confirm the weight of daily rainfall in farmland water balance. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the correlation between relative soil moisture between flat land and depression in the central area of ​​a certain province in Northeast China according to the present invention.

[0067] Figure 2 It is a schematic diagram of the correlation between relative soil moisture in hilly land and flat land in the central area of ​​a certain province in Northeast China according to the present invention.

[0068] Figure 3 The present invention discloses a precipitation distribution map of a certain town in a certain county in a certain city in Northeast China from 0:00 on August 3 to 0:00 on August 4.

[0069] Figure 4 It is a soil moisture content distribution map of a certain town in a certain county in a certain city in Northeast China from 0:00 on August 3 to 0:00 on August 4 after interpolation on a fine grid.

[0070] Figure 5 The present invention discloses a soil moisture distribution comparison diagram of a certain city and a certain county in Northeast China from 0:00 on August 2 to 0:00 on August 3, in which the moisture balance parameters are not corrected.

[0071] Figure 6 The present invention discloses a soil moisture distribution comparison diagram of a certain county in a certain city in Northeast China with the moisture balance parameters corrected from 0:00 on August 2 to 0:00 on August 3.

[0072] Figure 7 The present invention is a comparison diagram of drought and flood index and meteorological trend yield deviation from May to September in County A of a certain city in Northeast China.

[0073] Figure 8 The present invention is a comparison diagram of drought and flood index and meteorological trend yield deviation from May to September in County B of a certain city in Northeast China.

[0074] Fig. 9 It is a flow chart of the waterlogging forecasting method of the present invention.

[0075] Fig.10 It is a flow chart of the drought forecasting method of the present invention. DETAILED DESCRIPTION

[0076] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0077] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0078] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0079] The following is attached to this application specification Figure 1-10 , the technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0080] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0081] Implementation Method 1

[0082] This embodiment provides a method for dynamic monitoring and evaluation of corn drought and flood based on fine grid water balance. Fig. 9 As shown, the evaluation method comprises the following steps:

[0083] Step 1: Obtain the actual agricultural meteorological data, forecast data, geographic data and agricultural data of the region;

[0084] Step 2: Classify and store the data obtained in step 1 and build a database;

[0085] Step 3: Divide the data in the database in step 2 into a test set and a training set, and preprocess the training set;

[0086] Step 4: Construct a water budget parameter model based on different terrains of Gangpingwa;

[0087] Step 5: Based on the Penman algorithm, a polynomial fitting analysis method is used to construct a daily dynamic water requirement model for corn in the central area of ​​a certain province in Northeast China for the training set preprocessed in step 3;

[0088] Step 6: Based on steps 3 and 4, the interpolation analysis method is used to obtain the rainfall of farmland at the fine grid level;

[0089] Step 7: Construct a daily dynamic water surplus and deficit rate model based on steps 4 and 5;

[0090] Step 8: Based on step 7, the training set preprocessed in step 3 is subjected to a linear method to obtain the continuous rolling daily rainfall contribution weight and construct a daily dynamic farmland actual water supply index model;

[0091] Step 9: Construct a daily dynamic drought and flood index model based on steps 5 and 8;

[0092] Step 10: Based on the existing monthly corn drought and flood level threshold system and the industry standards of the China Meteorological Administration and the Ministry of Agriculture, the data of the test set in step 3 is substituted into the daily dynamic drought and flood index model in step 9 for calculation to obtain the daily corn field drought and flood level threshold system;

[0093] Step 11: Substitute the actual data in the test set data of step 3 into the daily dynamic drought and flood index model of step 9 for calculation to realize the monitoring of the daily dynamic drought and flood disaster drought and flood index.

[0094] Further, the step 1 is specifically that the meteorological elements of the actual data and forecast data include: temperature, maximum temperature, minimum temperature, precipitation, wind speed, maximum wind speed and maximum wind speed; mainly derived from national stations, meteorological encryption stations, self-built microclimate stations and fine grid multi-fusion simulation meteorological data, and also includes forecast data mainly derived from national information network forecast data, local meteorological department forecast data, and third-party website forecast data;

[0095] The geographic data includes the data spatial scale, if it is at the township level, the spatial resolution is less than 6.25 kilometers;

[0096] The agricultural data mentioned above refers to 30 years of corn production, disasters and growth and development data obtained by referring to the annual crop data of the county annals.

[0097] Furthermore, the step 2 specifically includes classifying and storing the data acquired in step 1 according to actual data, forecast data, geographic data and agricultural data, and performing multi-data fusion on the stored data;

[0098] Furthermore, the step 3 specifically includes dividing the fused data into a test set and a training set, and performing data preprocessing on the training set using an interpolation analysis method.

[0099] Specifically, the Kriging algorithm was used to interpolate the village level in a certain place in Northeast China to obtain 1 km grid point data.

[0100] Furthermore, the step 4 is specifically to use remote sensing technology to obtain the village-level elevation data (resolution 30m×30m) in the analysis area, follow the principle of high-altitude runoff output and low-lying confluence input of farmland, and establish a water budget parameter model for large, medium and small terrains in accordance with the linear correlation between rainwater budget and topography (elevation):

[0101]

[0102] Where: α is the water budget parameter of the plot (mostly between 0.7-1.3 in plain areas), α<1, high altitude, hilly land, less effective water; α>1, low altitude, low terrain, more effective water; h js is the mean value of the elevation data of the county where the plot is located; h jx is the mean value of the elevation data of the township level area where the plot is located; h jc h is the mean value of the elevation data of the village-level plot where the plot is located; j is the elevation data of the plot; e is the empirical coefficient (approximately equal to 1); m is the number of plots in the village.

[0103] Furthermore, in the case of no measuring station, the step 5 is to use the Penman algorithm to calculate and construct a daily dynamic water demand model based on the daily calculation results of the county-level meteorological station:

[0104]

[0105] Where: W k It is the daily sliding water requirement (mm) of each stage.

[0106] W i =4E -12 i 6 -2E -9 i 5 +3E -7 i 4 -E -5 i 3 +E -5 i 2 +0.0455i+0.1276 (3)

[0107] Where i is the day sequence value, which takes the value (1, 2, ..., d), where d = 365 during an ordinary year and d = 366 during a leap year.

[0108] in:

[0109] W=E0·λ (4)

[0110] Where: E0 is the reference evapotranspiration (mm / day), which is calculated daily according to the Penman method:

[0111]

[0112] Where: R n is the net radiation on the crop surface (MJ / (m2 day)); G is the soil heat flux (MJ / (m2day)); T is the daily average temperature at a height of 2 meters (℃); u2 is the wind speed at a height of 2 meters (m / s); e s is the saturated water vapor pressure (kPa); e a is the actual water vapor pressure (kPa); e s -e a is the saturated water vapor pressure difference (kPa); Δ is the slope of the saturated water vapor pressure curve; r is the hygrometer constant (kPa / ℃); λ is the corn crop coefficient, that is, the ratio of the water requirement of corn to the evaporation in the same period.

[0113] Furthermore, the step 6 specifically comprises obtaining the rainfall of farmland at a fine grid level by using an interpolation analysis method based on steps 3 and 4.

[0114] Furthermore, the step 7 is specifically as follows:

[0115] Step 7.1: Conduct targeted analysis on different agro-climatic zones through agro-geographic cluster analysis.

[0116] The actual water supply of farmland is obtained based on the water budget parameter model. After the soil moisture of Gangpingwa is processed, the actual water supply of farmland is obtained, which is calculated as follows:

[0117] U i =α×V i +G i (i=1,2,...) (6)

[0118] Where: U is the actual water supply to farmland (mm); V is the rainfall in farmland (mm); G is the irrigation amount (mm); i is the daily sequence value.

[0119] Step 7.2: Calculate the daily sliding water surplus and deficit based on the actual water supply and water demand of the farmland:

[0120]

[0121] H k =R k -W k (8)

[0122] Where: H is the daily sliding moisture surplus or deficit during the period (21 days) (mm), H≥0 is moisture surplus; H<0 is moisture deficit; R k is the actual water supply of farmland in the sliding day in the stage (21 days) (mm); k is the stage number; W k It is the daily sliding water requirement (mm) during the period (21 days).

[0123] Furthermore, the moisture profit and loss rate model is calculated based on the daily sliding moisture profit and loss amount:

[0124]

[0125] Where: P k is the moisture surplus or deficit ratio (%) during the daily sliding stage;

[0126] Furthermore, the daily rainfall impact weight of farmland water supply is obtained based on the water surplus and deficit ratio.

[0127] According to the principles of farmland hydrology, due to the continuous consumption of farmland water, the contribution of an effective daily rainfall to the effective water of farmland lasts for about 20 days on average. The farther the rainfall day is from the evaluation day, the smaller its contribution. The increase and decrease of farmland water is basically linearly related to time. Therefore, a linear method is used to determine the daily rainfall contribution weights as shown in the following table.

[0128] Table 1 Continuously rolling daily rainfall contribution weight table

[0129] q 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 <![CDATA[σ q ]]> 0.1 0.15 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.85 1.9

[0130]

[0131] Where: S k is the actual farmland water supply index with a sliding step of 21 days, q is the number of consecutive rolling days, σ q is the weight coefficient corresponding to the number of consecutive rolling days, U kq It is the actual water supply to farmland corresponding to 21 consecutive rolling days.

[0132] Furthermore, the drought and flood index is obtained based on the actual farmland water supply index, which is obtained by subtracting the daily sliding water demand of the stage (21 days) from the daily sliding farmland water supply index of the stage (21 days), and then dividing it by the daily sliding water demand of the stage (21 days);

[0133] The drought and flood index is specifically:

[0134]

[0135] Where: γ is the stage drought and flood index with a sliding step of 21 days

[0136] Furthermore, the step 10 is specifically as follows:

[0137] Step 10.1: Calculate the meteorological yield of corn in each county and city over the years (the yield variation affected by drought (flood));

[0138] Step 10.2: Statistically analyze the correlation between the water surplus and deficit ratio and meteorological output over the years, and make a comparative analysis of different drought and flood loss years to make it clear that the relationship is linearly correlated;

[0139] Step 10.3: According to the relevant industry standards of the China Meteorological Administration, the Ministry of Agriculture, etc., corn drought is divided into 4 levels, and a reduction of more than 5% is a drought year, a reduction of more than 20% is an extreme drought year, a reduction of less than 10% is a light drought, a reduction of 10-15% is a moderate drought, and a reduction of 15-20% is a severe drought. After calculation, it was found that the drought index of each county and city in the study area was between 0 and -1.1, and the waterlogging index was between 0 and 1.2; according to the principle of equal spacing, the new range of farmland water deficit rate corresponding to different drought levels was analyzed, and after years of field test data and typical drought and flood year data verification, the corn field water deficit rate threshold indicator system based on sliding time periods and daily weights for different drought levels in the main corn producing areas of a certain province in Northeast China was determined, as shown in Table 2:

[0140] Table 2 Thresholds for drought and flood levels of corn based on daily weighted water surplus and deficit ratios

[0141]

[0142] Verify the monitoring and evaluation results. Specifically,

[0143] 1. Verification of water budget parameters

[0144] Select counties and cities in the central region of a province in Northeast China, extract 20cm soil moisture data, analyze the correlation between hills, flat land, and depressions, and determine the water budget parameter α', such as Figure 1-2 shown.

[0145] The elevation data of a county under the jurisdiction of a city in the central region of a province in Northeast China was extracted, and the water balance parameter α was calculated for county-level plots and village-level plots, as shown in Table 3:

[0146] Table 3 Comparison of water budget parameters for plots of different levels

[0147]

[0148] It can be concluded that the error between the water budget parameters calculated from the elevation data and the water budget parameters calculated from the 20cm soil moisture is no more than 10%.

[0149] Next, the interpolation analysis method is used to obtain the distribution of soil moisture content under fine grids through the precipitation data of a certain town in a certain county in Northeast China from 0:00 on August 3, 2023 to 0:00 on August 4, 2023, as shown in the following figure: Figure 3-4 shown.

[0150] In addition, the precipitation data of a town in a county in a city in Northeast China from 0:00 on August 2 to 0:00 on August 3, 2023 were selected to draw the geographical distribution maps of soil moisture before and after the correction of water budget parameters for comparison. Figure 5-6 shown.

[0151] 2. Drought and flood index threshold verification

[0152] We selected two areas, County A and County B, in a city in Northeast China, and calculated the drought and flood index in severe drought years and severe flood years respectively, and compared them with the yield deviation of meteorological trend. We can see that the peak coincidence rate reaches more than 75%, such as Figure 7-8 shown.

[0153] This implementation method relies on multi-source data fusion technologies such as self-built meteorological stations, national meteorological stations, and numerical quantitative weather forecasts. It is based on remote sensing monitoring technology, through big data intelligent algorithm analysis, based on the principle of farmland water balance, and fully considers the elevation data of the plot area. It realizes real-time weather monitoring and evaluation, alarm, dynamic assessment of meteorological disasters and disaster trend prediction for the entire growth stage of corn, and realizes a refined mobile service model that integrates "intelligent perception", "intelligent assessment", "intelligent prediction", "intelligent release" and "intelligent application" of corn drought and flood disasters, providing drought and flood disaster prevention and control information for the entire process of corn production, and playing an important role in agricultural disaster prevention and loss reduction, stable production and increased yields.

[0154] Implementation Method 2

[0155] This embodiment provides a method for dynamically predicting corn drought and flood based on fine grid water balance. Fig.10 As shown, the prediction method includes the following steps:

[0156] Step 1: Obtain the actual agricultural meteorological data, forecast data, geographic data and agricultural data of the region;

[0157] Step 2: Classify and store the data obtained in step 1 and build a database;

[0158] Step 3: Divide the data in the database in step 2 into a test set and a training set, and preprocess the training set;

[0159] Step 4: Construct a water budget parameter model based on different terrains of Gangpingwa;

[0160] Step 5: Based on the Penman algorithm, a polynomial fitting analysis method is used to construct a daily dynamic water requirement model for corn in the central area of ​​a certain province in Northeast China for the training set preprocessed in step 3;

[0161] Step 6: Based on steps 3 and 4, the interpolation analysis method is used to obtain the rainfall of farmland at the fine grid level;

[0162] Step 7: Construct a daily dynamic water surplus and deficit rate model based on steps 4 and 5;

[0163] Step 8: Based on step 7, the training set preprocessed in step 3 is subjected to a linear method to obtain the continuous rolling daily rainfall contribution weight and construct a daily dynamic farmland actual water supply index model;

[0164] Step 9: Construct a daily dynamic drought and flood index model based on steps 5 and 8;

[0165] Step 10: Based on the existing monthly corn drought and flood level threshold system and the industry standards of the China Meteorological Administration and the Ministry of Agriculture, the data of the test set in step 3 is substituted into the daily dynamic drought and flood index model in step 9 for calculation to obtain the daily corn field drought and flood level threshold system;

[0166] Step 11: Substitute the actual data in the test set data of step 3 into the daily dynamic drought and flood index model of step 9 for calculation, and monitor the daily dynamic drought and flood disaster drought and flood index;

[0167] Step 12: Substitute the actual data and forecast data in the test set data of step 3 into the daily dynamic drought and flood index model of step 9 for calculation to realize the forecast of daily dynamic drought and flood disaster drought and flood index;

[0168] Step 13: Compare the forecast result of step 12 with the daily corn field drought and flood level threshold of step 10, and use the comparison result for guidance of drought and flood warning disasters.

[0169] Further, the step 1 is specifically that the meteorological elements of the actual data and forecast data include: temperature, maximum temperature, minimum temperature, precipitation, wind speed, maximum wind speed and maximum wind speed; mainly derived from national stations, meteorological encryption stations, self-built microclimate stations and fine grid multi-fusion simulation meteorological data, and also includes forecast data mainly derived from national information network forecast data, local meteorological department forecast data, and third-party website forecast data;

[0170] The geographic data includes the data spatial scale, if it is at the township level, the spatial resolution is less than 6.25 kilometers;

[0171] The agricultural data mentioned above refers to 30 years of corn production, disasters and growth and development data obtained by referring to the annual crop data of the county annals.

[0172] Furthermore, the step 2 specifically includes classifying and storing the data acquired in step 1 according to actual data, forecast data, geographic data and agricultural data, and performing multi-data fusion on the stored data;

[0173] Furthermore, the step 3 specifically includes dividing the fused data into a test set and a training set, and performing data preprocessing on the training set using an interpolation analysis method.

[0174] Specifically, the Kriging algorithm was used to interpolate the village level in a certain place in Northeast China to obtain 1 km grid point data.

[0175] Furthermore, the step 4 is specifically to use remote sensing technology to obtain the village-level elevation data (resolution 30m×30m) in the analysis area, follow the principle of high-altitude runoff output and low-lying confluence input of farmland, and establish a water budget parameter model for large, medium and small terrains in accordance with the linear correlation between rainwater budget and topography (elevation):

[0176]

[0177] Where: α is the water budget parameter of the plot (mostly between 0.7-1.3 in plain areas), α<1, high altitude, hilly land, less effective water; α>1, low altitude, low terrain, more effective water; h js is the mean value of the elevation data of the county where the plot is located; h jx is the mean value of the elevation data of the township level area where the plot is located; h jc h is the mean value of the elevation data of the village-level plot where the plot is located; j is the elevation data of the plot; e is the empirical coefficient (approximately equal to 1); m is the number of plots in the village.

[0178] Furthermore, in the case of no measuring station, the step 5 is to use the Penman algorithm to calculate and construct a daily dynamic water demand model based on the daily calculation results of the county-level meteorological station:

[0179]

[0180] Where: W k It is the daily sliding water requirement (mm) of each stage.

[0181] W i =4E -12 i 6 -2E -9 i 5 +3E -7 i 4 -E -5 i 3 +E -5 i 2 +0.0455i+0.1276 (3)

[0182] Where i is the day sequence value, which takes the value (1, 2, ..., d), where d = 365 during an ordinary year and d = 366 during a leap year.

[0183] in:

[0184] W=E0·λ (4)

[0185] Where: E0 is the reference evapotranspiration (mm / day), which is calculated daily according to the Penman method:

[0186]

[0187] Where: R n is the net radiation on the crop surface (MJ / (m2 day)); G is the soil heat flux (MJ / (m2day)); T is the daily average temperature at a height of 2 meters (℃); u2 is the wind speed at a height of 2 meters (m / s); e s is the saturated water vapor pressure (kPa); e a is the actual water vapor pressure (kPa); e s -e a is the saturated water vapor pressure difference (kPa); Δ is the slope of the saturated water vapor pressure curve; r is the hygrometer constant (kPa / ℃); λ is the corn crop coefficient, that is, the ratio of the water requirement of corn to the evaporation in the same period.

[0188] Furthermore, the step 6 specifically comprises obtaining the rainfall of farmland at a fine grid level by using an interpolation analysis method based on steps 3 and 4.

[0189] Furthermore, the step 7 is specifically as follows:

[0190] Step 7.1: Conduct targeted analysis on different agro-climatic zones through agro-geographic cluster analysis.

[0191] The actual water supply of farmland is obtained based on the water budget parameter model. After the soil moisture of Gangpingwa is processed, the actual water supply of farmland is obtained, which is calculated as follows:

[0192] U i =α×V i +G i (i=1,2,...) (6)

[0193] Where: U is the actual water supply to farmland (mm); V is the rainfall in farmland (mm); G is the irrigation amount (mm); i is the daily sequence value.

[0194] Step 7.2: Calculate the daily sliding water surplus and deficit based on the actual water supply and water demand of the farmland:

[0195]

[0196] H k =R k -W k (8)

[0197] Where: H is the daily sliding moisture surplus or deficit during the period (21 days) (mm), H≥0 is moisture surplus; H<0 is moisture deficit; R k is the actual water supply of farmland in the sliding day in the stage (21 days) (mm); k is the stage number; W k It is the daily sliding water requirement (mm) during the period (21 days).

[0198] Furthermore, the moisture profit and loss rate model is calculated based on the daily sliding moisture profit and loss amount:

[0199]

[0200] Where: P k is the moisture surplus or deficit ratio (%) during the daily sliding stage;

[0201] Furthermore, the daily rainfall impact weight of farmland water supply is obtained based on the water surplus and deficit ratio.

[0202] According to the principles of farmland hydrology, due to the continuous consumption of farmland water, the contribution of an effective daily rainfall to the effective water of farmland lasts for about 20 days on average. The farther the rainfall day is from the evaluation day, the smaller its contribution. The increase and decrease of farmland water is basically linearly related to time. Therefore, a linear method is used to determine the daily rainfall contribution weights as shown in the following table.

[0203] Table 1 Continuously rolling daily rainfall contribution weight table

[0204] q 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 <![CDATA[σ q ]]> 0.1 0.15 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.85 1.9

[0205]

[0206] Where: S k It is the actual water supply index of farmland in a period with a sliding step of 21 days.

[0207] Furthermore, the drought and flood index is obtained based on the actual farmland water supply index, which is obtained by subtracting the daily sliding water demand of the stage (21 days) from the daily sliding farmland water supply index of the stage (21 days), and then dividing it by the daily sliding water demand of the stage (21 days);

[0208] The drought and flood index is specifically:

[0209]

[0210] Where: γ is the stage drought and flood index with a sliding step of 21 days

[0211] Furthermore, the step 10 is specifically as follows:

[0212] Step 10.1: Calculate the meteorological yield of corn in each county and city over the years (the yield variation affected by drought (flood));

[0213] Step 10.2: Statistically analyze the correlation between the water surplus and deficit ratio and meteorological output over the years, and make a comparative analysis of different drought and flood loss years to make it clear that the relationship is linearly correlated;

[0214] Step 10.3: According to the relevant industry standards of the China Meteorological Administration and the Ministry of Agriculture, corn drought is divided into 4 levels. A reduction of more than 5% is a drought year, a reduction of more than 20% is an extreme drought year, a reduction of less than 10% is a light drought, a reduction of 10-15% is a moderate drought, and a reduction of 15-20% is a severe drought. After calculation, it was found that the drought index of each county and city in the study area was between 0 and -1.1, and the waterlogging index was between 0 and 1.2; according to the principle of equal spacing, the new range of farmland water deficit rate corresponding to different drought levels was analyzed, and after years of field test data and typical drought and flood year data verification, a corn field water deficit rate threshold indicator system based on sliding time periods and daily weights for different drought levels in the main corn producing areas of a certain province in Northeast China was determined.

[0215] Furthermore, the step 11 is specifically to obtain the drought and flood index based on the dynamic monitoring and prediction method of corn drought and flood based on the water balance of fine grids in different terrains, calculate the daily drought and flood index, and use the daily numerical weather forecast results to calculate the daily drought and flood index forecast value for the next 7 days.

[0216] Further, the step 12 is specifically to calculate the predicted value of the daily drought and flood index for the next 7 days based on the daily numerical weather forecast results, and obtain the guidance of the intelligent disaster degree of daily dynamic drought and flood warning, such as Figure 9-10 shown.

[0217] This implementation method relies on multi-source data fusion technologies such as self-built meteorological stations, national meteorological stations, and numerical quantitative weather forecasts. It is based on remote sensing monitoring technology, through big data intelligent algorithm analysis, based on the principle of farmland water balance, and fully considers the elevation data of the plot area. It realizes real-time weather monitoring and evaluation, alarm, dynamic assessment of meteorological disasters and disaster trend prediction for the entire growth stage of corn, and realizes a refined mobile service model that integrates "intelligent perception", "intelligent assessment", "intelligent prediction", "intelligent release" and "intelligent application" of corn drought and flood disasters, providing drought and flood disaster prevention and control information for the entire process of corn production, and playing an important role in agricultural disaster prevention and loss reduction, stable production and increased yields.

[0218] Fine grid: Meteorological data is refined to a spatial resolution of 1 km*1 km.

[0219] Dynamic monitoring method for drought and flood: A new drought and flood assessment method based on water balance was established, including a set of daily rainfall weights in farmland water balance.

[0220] Growth stage: According to the suitability of corn growth in the north, it is divided into five growth stages, namely sowing stage, emergence stage, jointing stage, silking stage and maturity stage.

Claims

1. A method for dynamic monitoring and evaluation of corn drought and flood based on fine grid water balance, characterized in that: The evaluation method comprises the following steps: Step 1: Obtain the actual agricultural meteorological data, forecast data, geographic data and agricultural data of the region; Step 2: Classify and store the data obtained in step 1 and build a database; Step 3: Divide the data in the database in step 2 into a test set and a training set, and preprocess the training set; Step 4: Construct a water budget parameter model based on different terrains of Gangpingwa; Step 5: Based on the Penman algorithm, a polynomial fitting analysis method is used to construct a daily dynamic water requirement model for corn in the region for the training set preprocessed in step 3; Step 6: Based on steps 3 and 4, the interpolation analysis method is used to obtain the rainfall of farmland at the fine grid level; Step 7: Construct a daily dynamic water surplus and deficit rate model based on steps 4 and 5; Step 8: Based on step 7, the training set preprocessed in step 3 is subjected to a linear method to obtain the continuous rolling daily rainfall contribution weight and construct a daily dynamic farmland actual water supply index model; Step 9: Construct a daily dynamic drought and flood index model based on steps 5 and 8; Step 10: Based on the existing monthly corn drought and flood level threshold system and the industry standards of the China Meteorological Administration and the Ministry of Agriculture, the data of the test set in step 3 is substituted into the daily dynamic drought and flood index model in step 9 for calculation to obtain the daily corn field drought and flood level threshold system; Step 11: Substitute the actual data in the test set data of step 3 into the daily dynamic drought and flood index model of step 9 for calculation, so as to realize the monitoring of the daily dynamic drought and flood disaster drought and flood index; The step 4 is specifically to obtain the village-level elevation data of the analysis area and establish a water budget parameter model for large, medium and small terrains at each level: Where: α is the water budget parameter of the plot, α<1, high altitude, hilly land, less effective water; α>1, low altitude, low terrain, more effective water; h js is the mean value of the elevation data of the county where the plot is located; h jx is the mean value of the elevation data of the township level area where the plot is located; h jc h is the mean value of the elevation data of the village-level plot where the plot is located; j is the elevation data of the plot; e is the empirical coefficient; m is the number of plots in the village; Specifically, in the absence of a measuring station, based on the daily calculation results of the county-level meteorological station, the Penman algorithm is used to calculate and construct a daily dynamic water demand model: Where: W k is the daily sliding water demand of each stage, k is the number of stages; W i =4E -12 and 6 -2E -9 and 5 +3E -7 and 4 -E -5 and 3 +E -5 and 2 +0.0455i+0.1276 (3) Where: i is the day sequence value, which takes values ​​(1, 2, ..., d), where d = 365 during a common year and d = 366 during a leap year; in: W=E0·λ (4) Where: λ is the corn crop coefficient, that is, the ratio of corn water demand to evapotranspiration during the same period; E0 is the reference evapotranspiration, which is calculated daily according to the Penman method: Where: R n is the net radiation on the crop surface; G is the soil heat flux; T is the daily average temperature at a height of 2 meters; u2 is the wind speed at a height of 2 meters; e s is the saturated water vapor pressure; e a is the actual water vapor pressure; e s -e a is the saturated water vapor pressure difference; Δ is the slope of the saturated water vapor pressure curve; r is the hygrometer constant; The step 7 is specifically as follows: Step 7.1: Conduct targeted analysis on different agro-climatic zones through agro-geographic cluster analysis. The actual water supply of farmland is obtained based on the water budget parameter model. After the soil moisture of Gangpingwa is processed, the actual water supply of farmland is obtained, which is calculated as follows: U i =α×V i +G i (i=1,2,...) (6) Where: U is the actual water supply of farmland; V is the rainfall of farmland; G is the irrigation amount; i is the daily sequence value; Step 7.2: Calculate the daily sliding water surplus and deficit based on the actual water supply and water demand of the farmland: H k =R k -W k (8) Where: H is the daily sliding moisture surplus or deficit in the stage, H≥0 moisture surplus; H<0 moisture deficit; R k is the actual water supply of farmland sliding every day in each stage; k is the stage number; W k The daily sliding water demand for the stage; Furthermore, the moisture profit and loss rate model is calculated based on the daily sliding moisture profit and loss amount: Where: P k It is the water surplus and deficit ratio in the daily sliding stage; Specifically, step 8 is to obtain the daily rainfall impact weight of farmland water supply based on step 7 and the linear relationship between farmland water fluctuation and time, so as to construct a 21-day sliding farmland actual water supply index model: Where: S k is the actual water supply index of farmland in the current period, q is the number of consecutive rolling days, σ q is the weight coefficient corresponding to the number of consecutive rolling days, U kq The actual water supply to farmland corresponding to the consecutive rolling days; Specifically, step 9 is to obtain a drought and flood index based on step 5 and step 8. The drought and flood index is obtained by subtracting the daily sliding water demand of the stage from the daily sliding water supply index of the stage, and then dividing it by the daily sliding water demand of the stage. That is, the daily sliding drought and flood index model is specifically: Where: γ is the stage drought and flood index.

2. The method for dynamic monitoring and evaluation of corn drought and waterlogging according to claim 1, characterized in that: Specifically, the meteorological elements of the actual data and forecast data include: temperature, maximum temperature, minimum temperature, precipitation, wind speed, maximum wind speed and extreme wind speed; The geographic data includes a data spatial scale; The agricultural data mentioned above refers to 30 years of corn production, disasters and growth and development data obtained by referring to the annual crop data in the county annals.

3. The method for dynamic monitoring and evaluation of drought and waterlogging of corn according to claim 1, characterized in that: Specifically, the step 2 includes classifying and storing the data acquired in the step 1 according to actual data, forecast data, geographic data and agricultural data, and performing multi-data fusion on the stored data; Specifically, step 3 is to divide the fused data into a test set and a training set, and perform data preprocessing on the training set using an interpolation analysis method.

4. A method for dynamic prediction of corn drought and flood based on fine grid water balance, characterized in that: The prediction method uses the corn drought and flood dynamic monitoring and evaluation method based on fine grid water balance as claimed in any one of claims 1 to 3, and further includes step 12, substituting the actual data and forecast data in the test set data of step 3 into the daily dynamic drought and flood index model of step 9 for calculation, so as to realize the forecast of the daily dynamic drought and flood disaster drought and flood index; Step 13: Based on the forecast results of the drought and flood index in step 12, the forecast results are matched with the daily corn field drought and flood level thresholds in step 10 to guide disaster prevention decisions.

5. The method for dynamic prediction of corn drought and waterlogging according to claim 4, characterized in that: The step 13 is specifically to calculate the predicted value of the daily drought and flood index for the next 7 days according to the daily numerical weather forecast results, and obtain guidance for daily dynamic drought and flood warning disasters.

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